In production applications nowadays, a main bottleneck of the development of the three-dimensional techniques, such as computer aided design, reverse engineering, virtual reality, and three-dimensional animation and game, is that there is still no a convenient method for quickly obtaining a three-dimensional model stored in the computer. In recent years, the three-dimensional laser scanner is widely applied by virtue of its advantage of conveniently and flexibly acquiring three-dimensional surface data of a real object. After the researches of more than twenty years, the technique of reconstructing a three-dimensional model from relatively complete point cloud data has been mature. However, it is restricted from being a universal inverse reconstruction technique mainly because that a large-scale data missing of the point cloud data usually occurs, which cannot be solved by the existed hardware scanning device.
The so called point cloud model generally means that a three-dimensional scanning device emits scanning light to a surface of an object to be measured, and reflected light is received to calculate a set of three-dimensional coordinate points on the surface of the object. The so called point cloud three-dimensional reconstruction means based on certain point cloud model data, complementing and reconstructing grid data representing the source model, for the convenience of computer rendering and user interaction. But how to directly and quickly obtain a practical point cloud model by scanning the point cloud data is still a problem. The difficulty of the point cloud data processing is that the point cloud data is usually dispersed, with significant missing data, noises, and outliers. In addition, the point cloud completion is a recognized ill-posed problem, wherein all the methods deduce unknown information from already existed information, and no method can ensure that the region complemented using an algorithm is consistent with the source model.
The point cloud completion method in the prior art fits and fills the lost information using a quadric surface according to local information of the point cloud model. This method can well fill the holes in a small area, but cannot solve the large-scale data missing problem. Considering more global information is an important way to improve the existed completion method.